Financial Fraud Detection with Entropy Computing

2025-03-14

AI Summary

To address the training bottlenecks of traditional machine learning on high-dimensional, large-scale data in financial fraud detection, this paper proposes CVQBoost—a scalable, quantum-enhanced gradient boosting algorithm grounded in the Entropic Quantum Computing (EQC) paradigm, implemented on the Dirac-3 quantum hardware. CVQBoost is the first method to integrate EQC principles into the gradient boosting framework, achieving a balance between model expressivity and computational efficiency. Experiments across datasets ranging from 1M to 70M samples demonstrate that CVQBoost matches XGBoost’s AUC performance while delivering significantly faster training times and near-linear scalability. Crucially, compared to classical baselines—including RAPIDS AI-accelerated XGBoost—this work provides the first empirical validation of practical quantum advantage for EQC in real-world financial risk management scenarios.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We introduce CVQBoost, a novel classification algorithm that leverages early hardware implementing Quantum Computing Inc's Entropy Quantum Computing (EQC) paradigm, Dirac-3 [Nguyen et. al. arXiv:2407.04512]. We apply CVQBoost to a fraud detection test case and benchmark its performance against XGBoost, a widely utilized ML method. Running on Dirac-3, CVQBoost demonstrates a significant runtime advantage over XGBoost, which we evaluate on high-performance hardware comprising up to 48 CPUs and four NVIDIA L4 GPUs using the RAPIDS AI framework. Our results show that CVQBoost maintains competitive accuracy (measured by AUC) while significantly reducing training time, particularly as dataset size and feature complexity increase. To assess scalability, we extend our study to large synthetic datasets ranging from 1M to 70M samples, demonstrating that CVQBoost on Dirac-3 is well-suited for large-scale classification tasks. These findings position CVQBoost as a promising alternative to gradient boosting methods, offering superior scalability and efficiency for high-dimensional ML applications such as fraud detection.

Problem

• Develops CVQBoost for efficient financial fraud detection.
• Compares CVQBoost with XGBoost on runtime and accuracy.
• Tests CVQBoost scalability on large synthetic datasets.

Innovation

• CVQBoost leverages Quantum Computing Inc's EQC paradigm.
• CVQBoost reduces training time significantly on large datasets.
• CVQBoost maintains competitive accuracy with superior scalability.